Cheminformatic models based on machine learning for pyruvate kinase inhibitors of Leishmania mexicana

Abstract

Background: Leishmaniasis is a neglected tropical disease which affects approx. 12 million individuals worldwide and caused by parasite Leishmania. The current drugs used in the treatment of Leishmaniasis are highly toxic and has seen widespread emergence of drug resistant strains which necessitates the need for the development of new therapeutic options. The high throughput screen data available has made it possible to generate computational predictive models which have the ability to assess the active scaffolds in a chemical library followed by its ADME/toxicity properties in the biological trials.

Results: In the present study, we have used publicly available, high-throughput screen datasets of chemical moieties which have been adjudged to target the pyruvate kinase enzyme of L. mexicana (LmPK). The machine learning approach was used to create computational models capable of predicting the biological activity of novel antileishmanial compounds. Further, we evaluated the molecules using the substructure based approach to identify the common substructures contributing to their activity.

Conclusion: We generated computational models based on machine learning methods and evaluated the performance of these models based on various statistical figures of merit. Random forest based approach was determined to be the most sensitive, better accuracy as well as ROC. We further added a substructure based approach to analyze the molecules to identify potentially enriched substructures in the active dataset. We believe that the models developed in the present study would lead to reduction in cost and length of clinical studies and hence newer drugs would appear faster in the market providing better healthcare options to the patients.

Figure 1

Comparison of Accuracy and Balanced Classification Rate of the models generated in the present study.

Figure 2

Plot of Sensitivity and Specificity of models generated based on molecular descriptors.

Figure 3

ROC plot showing significant AUC values for Random Forest, Naive Bayes, J48 and SMO classifiers.

Figure 4

Molecular alignment of the 7 [[1]-[7]] enriched substructures (dark green) over the top 20 molecules of the active (1087) dataset obtained from PubChem (AID 1721).